How Developers Can Use AI for Coding: 10 Practical Use Cases

Artificial intelligence is becoming a practical part of modern software development.

Developers can use AI to explain unfamiliar code, generate test cases, troubleshoot errors, write documentation, refactor existing code, and explore different implementation approaches.

But there is an important distinction between using AI to help write software and letting AI write software without review.

AI-generated code can contain bugs, incorrect assumptions, outdated approaches, security problems, or dependencies that don't belong in your project. GitHub's own guidance recommends reviewing and testing AI-generated code and checking that it fits the project's architecture, requirements, dependencies, and security practices.

The most useful approach is therefore:

Developer decides → AI assists → Developer verifies → Tests validate

In this guide, we'll look at 10 practical ways developers can use AI throughout the software development lifecycle.

1. Understand an Unfamiliar Codebase

One of the most useful applications of AI is helping you understand code you didn't write.

This is especially useful when joining an existing project, working with legacy code, or investigating a module you haven't touched before.

Instead of asking:

Explain this code.

Give the AI more context and a specific goal.

For example:

Explain this Java service class. Identify its main responsibility, the methods it exposes, the external dependencies it uses, and the sequence of operations when the main method is called. Do not suggest changes yet.

A good explanation should help you understand:

• What the class does
• Why it exists
• What calls it
• What it calls
• What data it receives
• What data it produces
• Where errors may occur
• Which dependencies are involved

You can then ask follow-up questions.

For example:

Now explain the same class from the perspective of someone debugging a production issue.

This is more useful than asking AI to rewrite the entire class immediately.

Why this helps

Understanding existing code is often more time-consuming than writing a small amount of new code.

AI can act as a conversational layer over unfamiliar code, helping you investigate it step by step.

However, always compare AI's explanation with the actual implementation. An AI model may infer behavior that isn't actually present.

2. Generate a First Draft of Code

AI can generate an initial implementation from a clear requirement.

For example, suppose you need a Spring Boot REST endpoint.

Instead of:

Create an API for users.

Give the AI specific requirements:

Create a Spring Boot REST endpoint to retrieve a user by ID. Use a controller, service, and repository structure. Return HTTP 404 when the user does not exist. Use a DTO for the response rather than exposing the persistence entity directly. Include a sample unit test.

The additional context gives the AI more constraints to work with.

The generated code should be treated as a starting point, not automatically production-ready code.

Review:

• Architecture
• Error handling
• Validation
• Security
• Naming
• Logging
• Transactions
• Dependencies
• Performance
• Test coverage

The developer remains responsible for deciding whether the implementation belongs in the project.

3. Debug Errors Faster

Developers regularly spend time interpreting stack traces and error messages.

AI can help turn a large error message into a more manageable investigation.

For example:

Here is the exception and the relevant service method. Explain the most likely causes, identify which line is most relevant, and give me a debugging checklist. Don't assume that the first possible cause is the actual cause.

This is better than asking:

Fix this error.

A useful debugging workflow is:

Error → Context → Possible causes → Tests → Fix → Verification

AI can help generate hypotheses, but the developer should verify those hypotheses against the actual application.

For example, if a database error occurs, AI may suggest several possible causes:

• Incorrect SQL
• Missing column
• Incorrect schema
• Transaction problem
• Connection configuration
• Serialization issue

Instead of immediately applying the suggested fix, test the possibilities.

This turns AI into a debugging assistant rather than a guessing machine.

4. Generate Unit Tests

Writing tests is one of the areas where AI can save developers significant time.

Suppose you have a service method with several conditions.

You can ask:

Create unit tests for this Java service method. Cover the normal case, missing data, invalid input, exceptions from the dependency, and boundary conditions. Use the testing framework already present in this project.

AI can help identify cases that you may have overlooked.

For example:

Scenario	Expected Result
Valid input	Successful response
Missing record	Expected not-found behavior
Invalid input	Validation error
Dependency failure	Appropriate exception handling
Boundary value	Correct handling
Null or empty value	Defined behavior

Don't simply accept the generated tests.

A common problem is writing tests that confirm what the implementation currently does rather than what the application is actually supposed to do.

Ask:

Do these tests verify the business requirement, or are they only verifying the current implementation?

That distinction matters.

5. Explain and Improve Existing Code

AI can also help identify areas of existing code that may be difficult to maintain.

For example:

Review this method for readability and maintainability. Identify unnecessary complexity, duplicated logic, unclear naming, and possible edge cases. Do not rewrite it yet.

This gives you an analysis before making changes.

You can then ask:

Suggest a refactoring that improves readability without changing the behavior. Explain each significant change.

This two-stage approach is safer than immediately asking AI to rewrite a large class.

For complex applications, provide relevant project conventions and architecture information.

AI-generated changes are more useful when the model understands the constraints of the existing codebase.

6. Generate Documentation

Developers often postpone documentation because writing it takes time.

AI can help convert technical information into a first draft.

For example, you can provide a REST controller and ask AI to create an initial API description containing:

• Endpoint
• HTTP method
• Parameters
• Request body
• Response
• Error conditions
• Example request
• Example response

You can also use AI to create:

• README sections
• API documentation
• Configuration explanations
• Setup instructions
• Architecture summaries
• Code comments
• Release notes

The developer should still verify that the documentation matches the actual implementation.

Documentation that looks professional but describes behavior the application doesn't actually have is worse than incomplete documentation.

7. Review Code Before a Pull Request

AI can also act as an additional reviewer.

For example:

Review this pull request for potential bugs, edge cases, security concerns, maintainability problems, and missing tests. Focus on issues that could affect production behavior. Do not rewrite the code unless necessary.

GitHub's current documentation describes AI-assisted code review as a way to identify issues and suggest fixes, while emphasizing that developers still need to evaluate and validate those suggestions.

A useful review checklist includes:

Functionality

• Does the change solve the intended problem?
• Are edge cases handled?
• Could the change break existing behavior?

Security

• Are credentials exposed?
• Is user input validated?
• Could the change introduce injection vulnerabilities?
• Are authorization checks present?

Reliability

• What happens when an external service fails?
• What happens when data is missing?
• Are retries appropriate?

Maintainability

• Is the implementation unnecessarily complicated?
• Does it follow existing project patterns?
• Are names and responsibilities clear?

Testing

• Are important paths covered?
• Are negative cases included?
• Are existing tests still meaningful?

AI review should be treated as an additional layer of review, not as a replacement for human code review.

8. Find Potential Security Problems

AI can help developers think through potential security issues in code.

For example:

Review this authentication-related code for common security weaknesses. Identify potential issues and explain how I can verify each one. Do not assume that a vulnerability exists unless the code provides evidence.

This can help create a security checklist.

For example, you might investigate:

• Hard-coded credentials
• SQL injection
• Missing authorization checks
• Unsafe input handling
• Sensitive information in logs
• Insecure file handling
• Weak validation
• Improper error messages
• Dependency risks

However, security review requires more than asking an AI model whether code is secure.

Automated security tools, dependency scanning, static analysis, testing, and human review should remain part of the process.

GitHub specifically recommends checking AI-generated code for vulnerabilities, dependency problems, hallucinated packages, and other issues.

9. Compare Different Implementation Approaches

Sometimes the hardest part of development isn't writing code.

It's deciding which approach to use.

For example, you might be deciding between:

• Synchronous vs. asynchronous processing
• REST vs. messaging
• Database query vs. caching
• One service vs. multiple services
• Different data structures
• Different testing approaches

Instead of asking AI:

Which one is better?

Give it the actual constraints.

For example:

Compare these two approaches for a Spring Boot application processing approximately 10,000 requests per minute. Consider latency, failure handling, operational complexity, maintainability, and testing. Explain when each approach would be appropriate.

The AI can then help structure the trade-offs.

A useful comparison looks like:

Factor	Approach A	Approach B
Complexity	Lower	Higher
Performance	Good	Potentially higher
Maintenance	Easier	More involved
Failure handling	Simpler	More complex
Testing	Easier	More involved

The important part is that you make the final architectural decision.

AI can organize the trade-offs, but it doesn't know every requirement, organizational constraint, production history, or future business need.

10. Learn a New Technology or Framework

AI can be useful when learning something unfamiliar.

Suppose you're a Spring Boot developer learning a new framework.

Instead of asking:

Teach me this framework.

Break the learning process into smaller tasks.

For example:

I already understand Java and Spring Boot. Explain this framework by comparing its concepts with Spring Boot where the comparison is useful. Start with dependency injection and request handling. Then give me a small example.

You can continue with progressively more difficult questions.

A useful learning sequence is:

Concept → Simple example → Modify example → Debug example → Build small project

This approach turns AI into an interactive learning assistant.

You can also ask:

Give me a small exercise. Don't provide the solution until I attempt it.

That encourages active learning instead of simply copying generated code.

A Better Prompt Pattern for Developers

The quality of an AI coding response often depends on the quality of the context you provide.

A useful structure is:

Goal + Context + Constraints + Existing Code + Expected Output

For example:

Goal: Add pagination to the customer API.

Context: This is a Spring Boot application using Spring Data JPA.

Constraints: Keep the existing endpoint structure and don't change the database schema.

Existing code: [relevant controller/service/repository]

Expected output: Explain the changes first, then provide the updated code and unit tests.

This is much more useful than:

Add pagination.

For more techniques, see our guide on how to write better AI prompts.

Give AI the Right Context

One of the biggest mistakes developers make is providing too little context.

Imagine asking AI to fix a method without explaining:

• What the method is supposed to do

• Which framework is being used

• Which Java version is used

• What the database looks like

• What constraints exist

• What error is occurring

• What behavior is expected

The model may generate technically valid code that is completely wrong for the project.

Instead, provide the minimum relevant context.

For a coding problem, this might include:

• Relevant source code

• Error message

• Expected behavior

• Current behavior

• Framework

• Language/version

• Important dependencies

• Constraints

Avoid dumping an entire repository into a prompt if only a few files are relevant.

Focused context generally makes the task easier to reason about.

Don't Let AI Rewrite Everything

One of the easiest ways to create problems is to ask AI to rewrite a large portion of a codebase without understanding the consequences.

A better approach is incremental.

For example:

Bad approach:

Rewrite this entire application using best practices.

Better approach:

Identify the three highest-risk areas in this service. Explain why they are problematic and suggest a minimal improvement for each. Do not modify the code yet.

Then address one issue at a time.

This makes the changes easier to understand, review, test, and reverse.

Always Test AI-Generated Code

AI-generated code can look convincing even when it is incorrect.

GitHub's documentation explicitly recommends functional checks, automated tests, static analysis, dependency review, and human validation when working with AI-generated code.

A practical workflow is:

Generate → Read → Compile → Test → Review → Run → Monitor

Don't stop at:

Generate → Copy → Commit

For production code, verify:

• It compiles

• Tests pass

• New tests are meaningful

• Existing tests still pass

• Dependencies are legitimate

• Security checks pass

• Error handling is appropriate

• Performance is acceptable

• Behavior matches the requirement

Watch for AI-Specific Coding Problems

AI coding assistants can make mistakes that are easy to overlook because the output looks professional.

Common problems include:

Hallucinated APIs

AI may reference a method, class, library, or configuration option that doesn't actually exist.

Incorrect Dependencies

An AI tool may suggest a package that sounds appropriate but is outdated, incorrect, or unnecessary.

Outdated Approaches

The generated solution may reflect an older framework version or deprecated API.

Overengineering

A simple problem can become a complicated implementation because the AI introduces unnecessary abstractions.

Incorrect Assumptions

AI may assume business rules that were never provided.

Missing Edge Cases

The main scenario may work while unusual inputs fail.

Security Problems

Code can compile successfully and still contain vulnerabilities.

These are reasons to treat AI-generated code as proposed code, not automatically trusted code.

AI Coding Tools Work Best as Assistants

AI coding tools are most useful when they accelerate work that developers already understand.

Think of AI as a combination of:

• Coding assistant

• Debugging partner

• Documentation assistant

• Test-generation assistant

• Research assistant

• Code-review assistant

• Learning assistant

It should not replace engineering judgment.

A developer still needs to understand:

• What the application does

• Why the code exists

• What the requirements are

• What can go wrong

• How the system should behave

• Whether the implementation is secure

The better you understand the problem, the more useful AI becomes.

A Practical AI Coding Workflow

Here's a simple workflow you can use for everyday development.

Step 1: Define the Problem

Write down what you're trying to accomplish.

Step 2: Gather Context

Collect the relevant code, error messages, requirements, and constraints.

Step 3: Ask AI for an Analysis

Before asking for code, ask AI to explain the problem and identify possible approaches.

Step 4: Choose an Approach

Review the suggestions and select the approach that fits your project.

Step 5: Generate a Small Change

Ask AI to implement a focused change rather than rewriting everything.

Step 6: Review the Code

Read the generated code yourself.

Step 7: Run Tests

Compile and run automated tests.

Step 8: Check Security and Dependencies

Look for vulnerabilities, unnecessary dependencies, and unsafe assumptions.

Step 9: Review the Final Diff

Compare exactly what changed.

Step 10: Commit Only What You Understand

If you cannot explain a significant part of the generated change, don't blindly commit it.

This workflow keeps the developer in control while still gaining much of the productivity benefit of AI.

How AI Fits Into the Software Development Lifecycle

AI can help at several points in the development lifecycle.

Development Stage	How AI Can Help
Requirements	Clarify and organize requirements
Planning	Compare implementation approaches
Coding	Generate initial implementations
Debugging	Analyze errors and suggest hypotheses
Testing	Generate test cases
Review	Identify possible issues
Documentation	Create documentation drafts
Learning	Explain unfamiliar concepts
Maintenance	Help understand legacy code
Automation	Reduce repetitive development tasks

The biggest benefit isn't necessarily writing code faster.

It can be reducing the amount of time developers spend switching between small repetitive tasks.

For example, generating a test skeleton, explaining an unfamiliar method, creating documentation, and preparing a first draft of a pull request can all reduce friction in the development workflow.

For broader workplace applications, see our guide on how to use ChatGPT for work.

When Should Developers Not Use AI?

AI isn't always the best choice.

You may want to work directly when:

• The task is extremely simple.
• You already know the solution faster than you can explain it.
• The code involves highly sensitive information.
• The generated output requires more verification than writing the code yourself.
• You are working on safety-critical or highly regulated systems.
• The task requires deep understanding of undocumented business logic.

The goal isn't to use AI for every line of code.

The goal is to use it where it creates a genuine advantage.

Final Thoughts

AI can become a valuable part of a developer's workflow when it is used deliberately.

It can help you understand unfamiliar code, generate first drafts, debug errors, create tests, improve documentation, review changes, explore architectural alternatives, and learn new technologies.

But AI-generated code should not automatically be considered production-ready.

The most reliable approach is:

Understand the problem → Give AI relevant context → Generate or analyze → Review → Test → Validate → Deploy

This keeps engineering judgment where it belongs: with the developer.

As AI coding tools continue to improve, the developers who benefit most may not be those who simply generate the most code. They will be the developers who know what to ask, what context to provide, what to verify, and when not to trust the first answer.

Frequently Asked Questions

Can AI write production-ready code?

AI can generate code that may be suitable for production after appropriate review and testing, but generated code should not automatically be assumed to be production-ready.

Is AI replacing software developers?

AI can automate parts of software development, but developers still need to understand requirements, architecture, security, testing, business logic, and system behavior. AI is better viewed as a development tool than a complete replacement for engineering judgment.

Can AI help debug Java and Spring Boot applications?

Yes. AI can help interpret stack traces, analyze relevant code, identify possible causes, suggest debugging steps, and propose fixes. The developer should verify the suggested cause and test any proposed change.

Can AI generate unit tests?

Yes. AI can generate test cases and test code based on existing implementations and requirements. Developers should review whether the tests actually verify the intended behavior and include important edge cases.

Should I give an AI coding assistant my entire repository?

Not necessarily. Providing only the relevant context can make the task more focused and can reduce unnecessary exposure of code or sensitive information. Follow your organization's policies for source-code and confidential-data handling.

Can AI review pull requests?

Yes. Modern AI coding tools can assist with code review and identify potential bugs, security issues, maintainability concerns, and missing tests. AI review should supplement rather than replace appropriate human review.

How can I get better coding results from AI?

Give the AI a clear goal, relevant project context, constraints, existing code, expected behavior, and desired output format. Then ask it to explain or analyze the approach before generating a large change.

What is the biggest mistake developers make when using AI?

Blindly accepting generated code is one of the biggest mistakes. AI output can contain incorrect logic, outdated APIs, security vulnerabilities, or assumptions that don't match the project. Review and testing remain essential.